In advanced patterning, such as, for example, pattering beyond 7 nm node using extreme ultra-violet (EUV) lithography, stochastic effects play a major role in total edge placement error (EPE). Stochastic effects are generally the result of events having random variables. For example, in the case of EUV, photon reactions with photoresists may vary with respective interactions of EUV light with the photoresists, causing variations in the design of a semiconductor device. For example, EPE refers to differences between intended and printed features of an integrated circuit (IC).
Technology computer aided design (TCAD) refers to the use of computer simulations to model and optimize semiconductor fabrication technologies and device operation. The electronic design automation that models fabrication has been referred to as process TCAD, the electronic design automation that models device operation has been referred to as device TCAD.
Understanding and accurate modeling of stochastic EPE (SEPE) is crucial to process control and failure analysis in semiconductor manufacturing. It is, therefore, important to accurately model developed critical dimension (DCD) distribution at the full-chip level. Current simulation solutions utilize production software which uses one model for critical dimension (CD) mean, and another separate empirical model for SEPE.
The conventional simulation models incorrectly assume a symmetrical distribution of CD, which has been proven not to be true, and three standard deviations (3σ) of CD mean depend only on dose sensitivity (ILS−1) or blurred ILS, which is an oversimplified empirical model. Further, simulation with physical models using commercially available software is computationally expensive and cannot be scaled to predict full-chip level CD deviation and thus yield.
Accordingly, there is a need for improved TCAD for semiconductor processing simulations, which addresses the above limitations.